# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
'''export'''
import os
import numpy as np

import mindspore
from mindspore import context, Tensor
from mindspore.train.serialization import export, load_checkpoint, load_param_into_net

from src.model import HED
from src.model_utils.config import config
from src.model_utils.moxing_adapter import moxing_wrapper

def modelarts_pre_process():
    '''modelarts pre process function.'''
    config.file_name = os.path.join(config.output_path, config.file_name)

@moxing_wrapper(pre_process=modelarts_pre_process)
def run_export():
    '''run export.'''
    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
    if config.device_target == "Ascend":
        context.set_context(device_id=config.device_id)

    h, w = config.testing_shape

    network = HED()
    network.set_train(False)

    param_dict = load_checkpoint(config.ckpt_file)
    load_param_into_net(network, param_dict)

    input_data = Tensor(np.zeros([config.batch_size, 3, h, w]), mindspore.float32)

    export(network, input_data, file_name=config.file_name, file_format=config.file_format)


if __name__ == "__main__":
    run_export()
